Experimental Study III

2018 ◽  
pp. 133-150
Keyword(s):  
Experimental Study ◽  
Forest Cover ◽  
Aggregate Data ◽  
Data Set ◽  
Forest Cover Type ◽  
Cover Type ◽  
Proposed Model ◽  
Type Data ◽  
Nominal Variables ◽  
Pattern Prediction

This chapter provides implementation of the proposed model on Forest Cover Type data set. The chapter includes the implementation of pattern extraction from this dataset by following a series of steps discussed in the proposed model chapter. It also includes detailed implementation of pattern prediction from Automobile dataset for prediction of numeric variables, nominal variables, and aggregate data. The implementation of pattern prediction is also a series of steps as discussed before.

Experimental Study II

2018 ◽  
pp. 111-132
Keyword(s):  
Experimental Study ◽  
Aggregate Data ◽  
Pattern Extraction ◽  
Data Set ◽  
Proposed Model ◽  
Nominal Variables ◽  
Pattern Prediction

This chapter provides an experimental study of the proposed model on Adult data set. The chapter includes the implementation of pattern extraction from this dataset by following a series of steps as discussed before. It also includes detailed implementation of pattern prediction of numeric variables, nominal variables, and aggregate data. The implementation of pattern prediction is also a series of steps as discussed before.

Experimental Study I

2018 ◽  
pp. 91-110
Keyword(s):  
Experimental Study ◽  
Aggregate Data ◽  
Pattern Extraction ◽  
Data Set ◽  
Proposed Model ◽  
Nominal Variables ◽  
Pattern Prediction

This chapter provides implementation of the proposed model on Automobile data set. The chapter includes the implementation of pattern extraction from this dataset by following a series of steps discussed in the proposed model chapter. It also includes detailed implementation of pattern prediction from Automobile dataset for prediction of numeric variables, nominal variables, and aggregate data. The implementation of pattern prediction is also a series of steps as discussed before.

scholarly journals A Robust and Reversible Watermarking Algorithm for a Relational Database Based on Continuous Columns in Histogram

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1994
Author(s):  
Yan Li ◽  
Junwei Wang ◽  
Hongyong Jia
Keyword(s):  
Experimental Data ◽  
Relational Database ◽  
Experimental Verification ◽  
Forest Cover ◽  
Reversible Watermarking ◽  
Prediction Errors ◽  
Data Set ◽  
Forest Cover Type ◽  
Data Distortion ◽  
Type Data

Due to the discreteness of integer data, there are a large number of gaps and continuous columns in the histogram based on integer data. Aiming at the characteristics, this paper presents a robust and reversible watermarking algorithm for a relational database based on continuous columns in histogram. Firstly, it groups the database tuples according to the watermark length and the grouping key. Secondly, it calculates the prediction errors and uses the absolute values of the prediction errors to construct the histogram. Thirdly, it traverses the histogram to find all the continuous columns and in turn, computes the sum of the height of each continuous column and selects the group of continuous columns that has the largest sum as the positions to embed the watermarks. FCTD (Forest cover type data set) is utilized for experimental verification. A large amount of experimental data shows that the method is effective and robust. Not only does the data distortion caused by shifting histogram columns not exist, but the robustness of the watermark is also greatly improved.

scholarly journals A reversible database watermarking method non-redundancy shifting-based histogram gaps

2020 ◽  
Vol 16 (5) ◽  
pp. 155014772092176 ◽  
Author(s):  
Yan Li ◽  
Junwei Wang ◽  
Xiangyang Luo
Keyword(s):  
Relational Databases ◽  
Forest Cover ◽  
Large Data ◽  
Histogram Shifting ◽  
Data Set ◽  
Forest Cover Type ◽  
Absolute Value ◽  
Data Distortion ◽  
Type Data ◽  
Distortion Rate

In relational databases, embedding watermarks in integer data using traditional histogram shifting method has the problem of large data distortion. To solve this problem, a reversible database watermarking method without redundant shifting distortion is proposed, taking advantage of a large number of gaps in the integer histogram. This method embeds the watermark bit by bit on the basis of grouping. First, an integer data histogram is constructed with the absolute value of the prediction error of the data as a variable. Second, the positional relationship between each column and the gap in the histogram is analyzed to find out all the columns adjacent to the gap. Third, the highest column is selected as the embedded point. Finally, a watermark bit is embedded on the group by the histogram non-redundant shifting method. Experimental results show that compared with existing reversible database watermarking methods, such as genetic algorithm and histogram shift watermarking and histogram gap–based watermarking, the proposed method has no data distortion caused by the shifting redundant histogram columns after embedding watermarks on forest cover type data set and effectively reduces the data distortion rate after embedding watermarks.

Stratification approach for forest cover type and landuse mapping using IRS-1A LISS-II data — A case study

1994 ◽  
Vol 22 (1) ◽  
pp. 21-29 ◽  
Author(s):  
S. Sudhakar ◽  
R. K. Das ◽  
D. Chakraborty ◽  
B. K. Bardhan Roy ◽  
A. K. Raha ◽  
...  
Keyword(s):  
Forest Cover ◽  
Forest Cover Type ◽  
Cover Type

scholarly journals ABOVE GROUND TREES BIOMASS OF LORE LINDU NATIONAL PARK-CENTRAL SULAWESI : A STUDY COMBINING FIELD MEASUREMENT AND REMOTE SENSING

Agromet ◽  
2010 ◽  
Vol 24 (1) ◽  
pp. 33
Author(s):  
Naimatu Solicha ◽  
Tania June ◽  
M. Ardiansyah ◽  
Antonius B. W.
Keyword(s):  
Remote Sensing ◽  
Carbon Stock ◽  
Field Data ◽  
Forest Cover ◽  
Satellite Image ◽  
Forest Biomass ◽  
Allometric Equation ◽  
Natural Forest ◽  
Forest Cover Type ◽  
Cover Type

Forests play an important role in global carbon cycling, since they hold a large pool of carbon as well as potential carbon sinks and sources to the atmosphere. Accurate estimation of forest biomass is required for greenhouse gas inventories and terrestrial carbon accounting. The information on biomass is essential to assess the total and the annual capacity of forest vigor. Estimation of aboveground biomass is necessary for studying productivity, carbon cycles, nutrient allocation, and fuel accumulation in terrestrial ecosystem. The possibility that above ground forest biomass might be determined from space is a promising alternative to ground-based methods. Remote sensing has opened an effective way to estimate forest biomass and carbon. By the combination of data field measurement and allometric equation, the above ground trees biomass possible to be estimated over the large area. The objectives of this research are: (1) To estimate the above ground tree biomass and carbon stock of forest cover in Lore Lindu National Park by combination of field data observation, allometric equation and multispectral satellite image; (2) to find the equation model between parameter that determines the biomass estimation. The analysis showed that field data observation and satellite image classification influencing much on the accuracy of trees biomass and carbon stock estimation. The forest cover type A and B (natural forest with the minor timber extraction) has the higher biomass than C and D (natural forest with the major timber extraction and agro forestry), it is about 607 ton/ha and 603 ton/ha. Forest cover type C is 457 ton/ha. Forest cover type D has the lowest biomass is about 203 ton/ha. Natural forest has high biomass, because of the tropical vegetation trees heterogeneity. Forest cover D has the lowest trees biomass because its vegetation component as secondary forest with the homogeneity of cacao plantation. The forest biomass and carbon estimation for each cover type will be useful for the further equation analysis when using the remote sensing technology for estimating the total biomass and for the economic carbon analysis.Forests play an important role in global carbon cycling, since they hold a large pool of carbon as well as potential carbon sinks and sources to the atmosphere. Accurate estimation of forest biomass is required for greenhouse gas inventories and terrestrial carbon accounting. The information on biomass is essential to assess the total and the annual capacity of forest vigor. Estimation of aboveground biomass is necessary for studying productivity, carbon cycles, nutrient allocation, and fuel accumulation in terrestrial ecosystem. The possibility that above ground forest biomass might be determined from space is a promising alternative to ground-based methods. Remote sensing has opened an effective way to estimate forest biomass and carbon. By the combination of data field measurement and allometric equation, the above ground trees biomass possible to be estimated over the large area. The objectives of this research are: (1) To estimate the above ground tree biomass and carbon stock of forest cover in Lore Lindu National Park by combination of field data observation, allometric equation and multispectral satellite image; (2) to find the equation model between parameter that determines the biomass estimation. The analysis showed that field data observation and satellite image classification influencing much on the accuracy of trees biomass and carbon stock estimation. The forest cover type A and B (natural forest with the minor timber extraction) has the higher biomass than C and D (natural forest with the major timber extraction and agro forestry), it is about 607 ton/ha and 603 ton/ha. Forest cover type C is 457 ton/ha. Forest cover type D has the lowest biomass is about 203 ton/ha. Natural forest has high biomass, because of the tropical vegetation trees heterogeneity. Forest cover D has the lowest trees biomass because its vegetation component as secondary forest with the homogeneity of cacao plantation. The forest biomass and carbon estimation for each cover type will be useful for the further equation analysis when using the remote sensing technology for estimating the total biomass and for the economic carbon analysis.

scholarly journals Estimates of Down Woody Materials on Fort A.P. Hill, Virginia

2008 ◽  
Vol 32 (2) ◽  
pp. 53-59 ◽  
Author(s):  
Jason R. Applegate
Keyword(s):  
Coarse Woody Debris ◽  
Forest Inventory ◽  
Forest Cover ◽  
Forest Type ◽  
Wildland Fire ◽  
Woody Debris ◽  
Forest Cover Type ◽  
Cover Type ◽  
Forest Cover Types ◽  
Structure Class

Abstract An inventory of down woody materials (DWM) was conducted on Fort A.P. Hill, Virginia, to develop a baseline of DWM abundance and distribution to assist in wildland fire management. Estimates of DWM are necessary to develop accurate assessments of wildfire hazard, model wildland fire behavior, and establish thresholds for retaining DWM, specifically CWD (coarse woody debris), as a structural component of forest ecosystems. DWM were sampled by forest type and structure class using US Forest Service, Forest Inventory and Analysis (FIA) field procedures. DWM averaged 12–16 tn/ac depending on forest cover type and structure class. Coarse woody debris (CWD) averaged 2.7–13.0 tn/ac depending on forest cover type and structure class. CWD comprised more than 70% of DWM across all forest cover types and structure classes. Fine woody debris (FWD) averaged 0.05–3.2 tn/ac depending on fuel hour class, forest cover type, and structure class. DWM was consistently higher in mature (sawtimber) forests than in young (poletimber) forests across all forest cover types, attributed to an increased CWD component of DWM. The variability associated with DWM suggests that obtaining robust estimates of CWD biomass will require a higher sampling intensity than FWD because of its nonuniform distribution in forest systems. FIA field procedures for tallying and quantifying DWM were practical, efficient, and, subsequently, included as permanent metrics in Fort A.P. Hill's Continuous Forest Inventory program.

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